为了提高高光谱图像的分类精度，提出了一种基于多尺度卷积神经网络的高光谱图像分类算法。首先，利用等距特征映射算法处理高光谱数据，以挖掘数据的非线性特性，保持数据点的内在几何性质；然后，构建以标记像元为中心的训练图像块，训练多尺度卷积神经网络；最后，利用 softmax分类器预测测试像元的标签。提出的方法在 Indian Pines、University of Pavia和 Salinas scene高光谱遥感数据集上进行分类实验，并与 CNN、R-PCA CNN、CNN-PPF、CD-CNN等算法进行性能比较。实验结果表明，在 3个数据集上提出的方法的总体识别精度分别达到 98.51%、98.64%和 99.39%，与 CNN算法相比分别提高了约 8.35%、6.37%和 7.81%。本文提出的方法无论是在分类精度还是 Kappa系数上都优于另外 4种方法，是一种较好的高光谱遥感数据分类方法。

To improve the classification accuracy of hyperspectral remote sensing images, a classification algorithm based on a multiscale convolutional neural network (CNN) is proposed. First, an isometric feature mapping algorithm was used to process hyperspectral data, to mine the nonlinear characteristics of the data and maintain the intrinsic geometric properties of data points. Second, training image blocks centered on labeled pixels were constructed, after which the multiscale CNNs were trained. Finally, the Softmax classifier was used to predict the label of the test pixel. The proposed method performed classification experiments on the Indian Pines, University of Pavia, and Salinas scene hyperspectral remote sensing datasets, and its performance was compared with a CNN, randomized principal component analysis (R-PCA CNN), a deep CNN with pixel-pair features (CNN-PPF), a cross-domain CNN (CD-CNN), and other algorithms. The experimental results showed that the overall recognition accuracy of the proposed method for the three datasets was 98.51%, 98.64%, and 99.39%, respectively, which was 8.35%, 6.37%, and 7.81% higher than that of the CNN algorithm, respectively. The proposed method performed better than the other four methods studied, in terms of both classification accuracy and Kappa coefficient, providing a superior method for hyperspectral remote sensing data classification.